Cognitive Science & Artificial Intelligence
According to Merriam-Webster’s online dictionary, cognitive science is defined as “an interdisciplinary science that draws on many fields (such as psychology, artificial intelligence, linguistics, and philosophy) in developing theories about human perception, thinking, and learning.” In my educational setting, a multi-district magnet high school, we use a hybrid model, or a blend of face-to-face and online learning in our classrooms. A large part of both sides of the hybrid model is collaboration, and we achieve a high level of collaboration by using Kagan (2009) Cooperative Learning structures. Willis (2009) asserts, “Cooperative group activities, unlike whole class discussions or independent work, provide the most opportunities for students to express their ideas, questions, conclusions, and associations verbally.” She goes on to state that, “When students participate in engaging learning activities in well-designed, supportive cooperative groups, their brain scans show facilitated passage of information from the intake areas into the memory storage regions of the brain” (Willis, 2009). Not only do Kagan structures increase cognitive function, therefore, but they also teach students crucial social skills that will help them be successful in a global society. Mercer (2013) posits, “…one of the most important functions of our social-cognitive capabilities, which is that we are able to engage together in goal-oriented, knowledge-building, and problem-solving activities. We do not only use reasoning as an individual weapon to resist other people’s agendas; we also use it in dialogues to find the best possible solutions to the problems we jointly encounter” (p. 151).
Thus far, Artificial Intelligence, or AI, is not able to implement collaboration in a classroom in the same way an educator can to improve student performance. In his video, Lecun (2013) discusses many aspects of human thinking that computers can perform, but also states that what computers are still lacking is thinking related to real world applications (which is the kind we want to teach our students). Computers struggle to be aware of the thoughts of others, navigate culturally diverse situations, and give students a feeling of being present with them. For example, when taking a brain break, I am able to participate with my students, whereas a computer could not.
When it comes to collaboration, adults and teenagers operate differently. According to Conlan, Grabowski, and Smith (2003), adult learners demonstrate the following characteristics: they are independent and direct their own learning, they have a host of life experiences, their learning needs change based on the social role they are filling, they are problem centered and want to apply new knowledge immediately, and they are intrinsically motivated. Adults rely more on themselves, rather than the group and can use their collective experiences to the benefit of the team as well. They can also perform a variety of roles because they are more flexible with their learning and they are motivated within themselves as opposed to needing a reward from an outside entity. Although it seems that adults would be more natural collaborators, this is not the case. Because they are used to relying on themselves, sometimes they do not share resources with others or if they have a certain way of operating, it is difficult for them to change to meet the needs of the team. Adolescents, on the other hand, are more dependent and need someone to help guide their learning. They are still collecting life experiences and they need to be taught various social skills and roles. Oftentimes, they are reluctant to apply knowledge and are extrinsically motivated. Loesser (2015) discusses aspects of cooperative learning, such as having to depend on one another, sharing materials, and learning how to interact (p. 8). These skills take a lot of explicit instruction and modeling before students can work interdependently and collaboratively. However, teens highly enjoy Kagan structures and often state that cooperative learning is the aspect of my classes that they enjoy the most.
Play Rock, Paper, Scissors with a computer (you can even see what the computer is thinking!): http://www.nytimes.com/interactive/science/rock-paper-scissors.html?_r=0
View a TED talk all about the adolescent brain: https://www.ted.com/talks/sarah_jayne_blakemore_the_mysterious_workings_of_the_adolescent_brain
Logic, Rules, Concepts, Analogies, & Images
Being able to apply logic, rules, concepts, analogies and images is a critical 21st century skill our students must master if they are going to be successful in college or career. Understanding how to use logic helps my students make valid inferences and balance inductive and deductive reasoning (Ash). Without logic, students would not be able to make predictions about what they are reading or formulate big ideas and thematic concepts. Without logic, students would not practice higher order thinking skills, and they would not be able to critically evaluate anything. Rules allow me to be consistent with my students and organized in my classroom. I am often complimented on having excellent classroom management, and I attribute this to having clear rules and expectations. I also use rules for teaching English language acquisition and problem solving. Concepts are abstract ideas or mental images (Ash). In every unit I teach, students must be able to conceptualize very abstract concepts so that they can make connections between the text and themselves, the text and other texts, and the text and the world. Ash discusses the importance of both verbal and visual depictions of concepts in order to foster creative thinking (thinking of new ways to describe or illustrate an idea) and critical thinking (thinking deeply in order to make connections and mentally balance multiple ideas at once). Visual learning, particularly using images, has been especially helpful for me when teaching vocabulary. Students are much more able to grasp the full meaning of a word when they have images to associate with it. Ultimately, it is crucial that students make connections in their brains for all of the dimensions on the learning style inventory because different tasks require different approaches. Goodman, Tenenbaum, Feldman, and Griffiths (2008) state, “In the context of concept learning, the integration of statistical learning and rule-based concept representations may help us to understand how people can induce richly structured concepts from sparse experience” (p. 146). Therefore, it is crucial that teachers understand all of these cognitive domains–logic, rules, concepts, analogies, and images–because they each play a vital role in being an effective educator and learner.
Test your analogy skills with this online game: http://www.vocabulary.co.il/analogies/high-school/high-school-analogy-match/
We are going to prefer the learning style that our brain has made the most connections with. Just because a person has a certain learning style preference does not mean they will have to utilize other methods in solving problems or learning. In the “How We Learn” video, the narrator states, “Learning something new means rearranging the way our brain works” (Streetwisdom Billy, 2010). Our brains have over one hundred billion neurons, and the goal is to create and strengthen pathways between them. In the video, they use the analogy of a human crossing a ravine in order to build a bridge. The first time across is quite difficult, but once you make that first trip across, it becomes easier and easier. It is the same with our brains–the more connections our brain makes, the easier it is to keep building these mental bridges. Information has to jump through the synapse if we are going to learn it, and it is not easy to get it to transfer. However, once it does, it makes a connection and then we can strengthen that connection. Therefore, it is important that educators differentiate materials to address many learning styles. I do this in my classroom by using a combination of strategies and resources.
Check out this article about integrating learning styles and multiple intelligences in your classroom: http://www.ascd.org/publications/educational-leadership/sept97/vol55/num01/Integrating-Learning-Styles-and-Multiple-Intelligences.aspx
Three Powerful Implications of Cognitive Science
The research behind cognitive science has three major implications in my setting. For one, collaboration makes a person stronger cognitively, so I will continue to use Kagan (2009) Cooperative Learning structures to support student academic and social growth. Secondly, since I understand that students need a blend of logic, rules, concepts, analogies, and images in order to be prepared for the real world, I will make sure my curriculum accounts for all of these domains. Finally, I will address students’ learning styles, while also pushing them to learn their non-dominant ones.
Ash, D. EDU510 The cognitive science of teaching & learning: Unit 2 logic, rules, & concepts [PowerPoint slides]. Retrieved from http://www.coursematerials.net/edu/edu510/unit2/index.htm.
Ash, D. EDU510 the cognitive science of teaching & learning: Unit 3 analogies/case & images [PowerPoint slides]. Retrieved from http://www.coursematerials.net/edu/edu510/unit3/index.htm.
Cognitive science [Def. 1]. (n.d.). In Merriam-Webster Online. Retrieved March 21, 2015, from http://www.merriam-webster.com/dictionary/cognitive%20science.
Conlan, J., Grabowski, S., & Smith, K. (2003). Emerging perspectives on learning, teaching, and technology: Adult learning. Retrieved from http://epltt.coe.uga.edu/index.php?title=Adult_Learning.
Costa, A. L., & Kallick, B. (2008). Learning and Leading with Habits of Mind: 16 Essential Characteristics for Success. Alexandria, VA: Association for Supervision and Curriculum Development.
Goodman, N. D., Tenebaum, J. B., Feldman, J., & Griffiths, T. L. (2008). A rational analysis of rule-based concept learning. Cognitive Science, 32, 108-154. doi: 10.1080/03640210701802071.
Kagan, S., & Kagan, M. (2009). Kagan Cooperative Learning. San Clemente, CA: Kagan Publishing.
Lecun, Y. (2013). The Rise of Artificial Intelligence. [Video file]. Retrieved from https://www.youtube.com/watch?v=53K1dMys1Jg&t=123.
Loesser, J. W. (2015). Cooperative learning. Research Starters Education, 1-8. Retrieved from http://eds.a.ebscohost.com/eds/pdfviewer/pdfviewer?sid=3397e754-a53f-4a6a-890d-e4f0e24dc207%40sessionmgr4005&vid=15&hid=4213.
Mercer, N. (2013). The social brain, language, and goal-directed collective thinking: A social conception of cognition and its implications for understanding how we think, teach, and learn. Educational Psychologist, 48(3), 148-168. doi: 10.1080/00461520.2013.804394.
Streetwisdom Billy. (2010). How we learn: Synapses and neural pathways [Video file]. Retrieved from https://www.youtube.com/watch?v=BEwg8TeipfQ.
Willis, J. (2009). Cooperative learning is a brain turn-on. Kagan Online Magazine. Retrieved from http://www.kaganonline.com/free_articles/research_and_rationale/310/Cooperative-Learning-is-a-Brain-Turn-On.